Project Summary The Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative promotes the development and application of technologies to describe the temporal and spatial dynamics of cell types and neural circuits in the brain. The Principal Investigator, senior personnel and staff of this project have diverse expertise required to marshall data across the BRAIN Initiative consortium, including experience in data collection from multiple institutions, large-scale quality control and analysis processing capability, familiarity with NIH policy and public archive deposition strategies. To promote smooth interactions across a large research consortium, we will develop the Neuroscience Multi-Omic Archive (NeMO Archive), a data repository that is specifically focused on the storage and dissemination of omic data from the BRAIN Initiative and related brain research projects. We will utilize a federated model for data storage such that the physical location of data can be distributed between the NeMO local file system, public repositories, and a cloud-based storage system (e.g. Amazon S3). We will leverage this capability and distribute BRAIN Initiative data between our local filesystem and the cloud. The Nemo Archive will be a data resource consistent with the principles advanced by research community members who are launching resources in next generation NIH data ecosystem. These practices include FAIR Principles, documentation of APIs, data-indexing systems, workflow sharing, use of shareable software pipelines and storage on cloud-based systems. The information incorporating into the NeMO archive will, in part, enable understanding of 1) genomic regions associated with brain abnormalities and disease; 2) transcription factor binding sites and other regulatory elements; 3) transcription activity; 4) levels of cytosine modification; and 5) histone modification profiles and chromatin accessibility. It will enable users to answer diverse questions of relevance to brain research, such as identifying diagnostic candidates, predicting prognosis, selecting treatments, and testing hypotheses. It will also provide the basic knowledge to guide the development and execution of predictive and machine learning algorithms in the future.